解决烧心最快方法喝牛奶（解决OSError: Error no file named pytorch_model.bin, tf_model.h5 found in directory）

问题：

OSError: Error no file named pytorch_model.bin, tf_model.h5, model.ckpt.index or flax_model.msgpack found in directory

出现过程：

使用transformers的Bertmodel时

问题代码：

model = Bertmodel.from_pretrained(bert_model)

问题原因：

下载的模型文件夹中没有pytorch_model.bin/tf_model.h5等文件   
    
    
    
   ，去原网址中查找也未发现相关文件    
    
    
    
。继而查看transformers官方使用说明：

from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)

pretrained_model_name_or_path – either: - a string with the shortcut name of a pre-trained model to load from cache or download, e.g.: bert-base-uncased. - a string with the identifier name of a pre-trained model that was user-uploaded to our S3, e.g.: dbmdz/bert-base-german-cased. - a path to a directory containing model weights saved using save_pretrained(), e.g.: ./my_model_directory/. - a path or url to a PyTorch state_dict save file (e.g. ./pt_model/pytorch_model.bin). In this case, from_pt should be set to True and a configuration object should be provided as config argument. This loading path is slower than converting the PyTorch checkpoint in a TensorFlow model using the provided conversion scripts and loading the TensorFlow model afterwards.

即：如果是加载用save_pretrained保存过的模型


     


     


     


     

，需要加入参数from_pt/from_tf和相应的config

解决方法：

# 模型文件夹中的config文件路径 config = BertConfig.from_json_file(./tf_model/my_tf_model_config.json) # 如果下载的是tensorflow模型 




 




 




 




，则from_tf=True;如果是pytorch模型   

   

   

   

  ，则参数设置改为from_pt=True model = BertModel.from_pretrained(./tf_model/my_tf_checkpoint.ckpt.index, from_pt=True, config=config)